EDET DAVID KOKOETTE, Prof. Onuh Spencer
The optimization of battery management systems (BMS) is essential for the effective operation of nano satellites, which require efficient energy management strategies to maximize their operational capabilities. This thesis aims to design and implement an optimized BMS tailored for nano satellites by employing various algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) to enhance battery efficiency and lifespan. An extensive literature review was conducted to identify existing methodologies, followed by the development of a simulation model utilizing MATLAB® R2021a for testing the proposed algorithms. The results indicated significant improvements in battery performance metrics, including increased State of Health (SOH) and efficiency in energy consumption compared to traditional methods. Iterative testing showed consistent reductions in operational costs of the lithium-ion batteries while ensuring reliable performance over extended periods. This research contributes to the field of aerospace engineering by providing novel insights into the integration of advanced optimization techniques within BMS frameworks, ultimately supporting the sustainable deployment of nano satellites in various applications. Furthermore, the findings underscore the importance of adaptive management systems in the rapidly evolving domain of satellite technology.
@article{c0f8ef53-80ee-49da-aa8d-1d3d3e08d9ef,
title={OPTIMIZATION OF BATTTERY},
author={EDET DAVID KOKOETTE and Prof. Onuh Spencer},
year={2021},
language={English}
}TY - JOUR TI - OPTIMIZATION OF BATTTERY AU - EDET DAVID KOKOETTE AU - Prof. Onuh Spencer PY - 2021 LA - English ER -
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